MEFISTO on pan-fetal immune
suppressPackageStartupMessages({
library(tidyverse)
library(MOFA2)
library(Matrix)
library(SingleCellExperiment)
library(scran)}
)[0;1;31mSystem has not been booted with systemd as init system (PID 1). Can't operate.[0m
[0;1;31mFailed to create bus connection: Host is down[0m
running command 'timedatectl' had status 1Load pseudobulked data
indir <- "/nfs/team205/ed6/data/Fetal_immune/LMM_data/LMM_input_MYELOID_PBULK/"
matrix <- readMM(file = paste0(indir, "matrix.mtx.gz"))
coldata <- read.csv(file = paste0(indir, "metadata.csv.gz")) %>%
column_to_rownames("X")
rowdata <- read.csv(file = paste0(indir, "gene.csv.gz"))
## Make SingleCellExperiment obj
myeloid_sce <- SingleCellExperiment(list(logcounts = t(matrix)), colData = coldata)
rownames(myeloid_sce) <- make.unique(rowdata$GeneName) Preprocessing
Exclude celltypes present in just one organ
keep_ct <- data.frame(colData(myeloid_sce)) %>%
select(organ, anno_lvl_2) %>%
distinct() %>%
group_by(anno_lvl_2) %>%
summarise(n=n()) %>%
ungroup() %>%
filter(n > 1) %>%
pull(anno_lvl_2)
myeloid_sce <- myeloid_sce[,myeloid_sce$anno_lvl_2 %in% keep_ct]
myeloid_sceclass: SingleCellExperiment
dim: 33694 1183
metadata(0):
assays(1): logcounts
rownames(33694): TSPAN6 TNMD ... RP11-107E5.4 RP11-299P2.2
rowData names(0):
colnames(1183): F21_LI_45P-F21-LI-ERY_MAC-16-3GEX F21_LI_45P-F21-LI-DC2-16-3GEX ... F66-FPI-0-SC-1-F66-GU-Mono_Mac-15-3GEX F66-FPI-0-SC-1-F66-GU-MAC-15-3GEX
colData names(6): Sample donor ... age method
reducedDimNames(0):
altExpNames(0):Feature selection: options here
- select HVGS with scran
- use same HVGS used for clustering (these options don’t maximize variation between organs)
- Find HVGS within each celltype and take union
# all_obs <- read_csv("/nfs/team205/ed6/data/Fetal_immune/PAN.A01.v01.entire_data_normalised_log.wGut.batchCorrected_20210118.")
## Feature selection w scran WITHIN CELLTYPE
anno_groups <- split(colnames(myeloid_sce), myeloid_sce$anno_lvl_2)
all_hvgs <- c()
for (i in anno_groups){
dec <- modelGeneVar(myeloid_sce[,i])
hvgs <- getTopHVGs(dec, n = 1000)
all_hvgs <- union(all_hvgs, hvgs)
}
myeloid_sce <- myeloid_sce[all_hvgs,]
myeloid_sce <- myeloid_sce[which(rowSums(logcounts(myeloid_sce)) > 0),]
myeloid_sceclass: SingleCellExperiment
dim: 4256 1183
metadata(0):
assays(1): logcounts
rownames(4256): HBG2 HBA2 ... MIEN1 SPPL2A
rowData names(0):
colnames(1183): F21_LI_45P-F21-LI-ERY_MAC-16-3GEX F21_LI_45P-F21-LI-DC2-16-3GEX ... F66-FPI-0-SC-1-F66-GU-Mono_Mac-15-3GEX F66-FPI-0-SC-1-F66-GU-MAC-15-3GEX
colData names(6): Sample donor ... age method
reducedDimNames(0):
altExpNames(0):Scale
assay(myeloid_sce, "scaled_logcounts") <- t(scale(t(logcounts(myeloid_sce))))EDA with PCA
```r
library(scater)
myeloid_sce <- runPCA(myeloid_sce, scale=FALSE, ncomponents=30)
## Variance explained
percent.var <- attr(reducedDim(myeloid_sce), \percentVar\)
plot(percent.var, log=\y\, xlab=\PC\, ylab=\Variance explained (%)\)
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RCUQAMEJdAAQQk0QFACDRCUQNfXjQfscsRTqYcAGoNA19Oir21x+R1n9L8i9RxAQxDoehr/qflt26fXeTP1IEAjEOh62ufq8rLXtYnnABqCQNfTLneUl9EXJp4DaAgCXU+jf1Jetr0z8RxAQxDoevqfAZOLxdbzNl2QehCgEQh0Xd0wcKcDN9v22dRjAA1BoOvr3Tsu/+/W1EMAjUGgAYISaICgBBogqNwCPXPyhAmPzu7w2wINkCGnQN8wqlDSc8eJHewg0AAZ8gn0FT32vvqBKVMmXbprzxva30OgATLkE+gRJy19NGZk+3sINECGfALd64NXNlp6t7+HQANkyCfQg89a+uiMoe3vIdAAGfIJ9Lm9jm6ZPmPGS3ePXn5c+3sINECGnK7iGDeofBVHYfhVHewg0AAZ8roOunXaTePH3/Jch98XaIAM3kkIEFTegd6u54f/9Pdf/WKJFTt4bRqAJfIO9EVjP/ynKd9eqte51T0fQNPwEgdAULkG+q3H3+voWwINkCGfQH9nWttm9tcLhZ7HLmx/D4EGyJBPoAstbZuxfX9y24krntf+HgINkCHHQG90TtvmtI3a30OgATLkGOi+D7Zt7nOzJIDq5Bjorf/YtvnN+u3vIdAAGXIK9G7HnHjmHtsUi1OGH9D+HgINkCGfQO+6w5ZD+/dZvVj88rDp7e8h0AAZ8rwOelGx+NTcDr4n0AAZvJMQICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBTubFPz+fegQgNIFO5LmdB+6wwQ5PpR4DCEyg05g95NyFxUUXD5qZehAgLoFO44rdy8t+FyWeAwhMoNP4/lnlZdyhiecAAhPoNE769/Lyk2MTzwEEJtBp3DdsTtt27sg7Uw8CxCXQiRz8iZueuXWb/VKPAQQm0Kn8duehO13dmnoKIDCBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEuju4L3W1BMACQh0eAvPH9R3lX2npx4D6HICHd53Pzu1+O6pG7yReg6gqwl0dM8OeK+0HHlc6kGAribQ0Y3/WnmZ8JnEcwBdTqCjE2hoWgId3ZKXOI7yEgc0HYEO77uffaI467T1/SMhNB2BDm/Bz9dfZZWvucwOmo9AdwfvLko9AZCAQAMEVUWgW/96zx/ueaLGNx8LNECGigM95RtrFUrWPmBqLccVaIAMFQb6/YN7fOqUe5547Yl7Tt5mue+8X/1xBRogQ4WB3mKbez/4yp9HbVn9cQUaIEOFgR674ENfWvCD6o8r0AAZqrmKo/WPR3z98D/U9q+EAg2QoZpAf3/w4f8+esixNR1XoAEyVBjo10qb9f/Rtnl3vZqOK9AAGSoM9OrnLigWN7mt7dHEITUdV6ABMlQY6Mlbj7y3eNtqa2+01jo313RcgQbIUOlr0K2/XPsbf5vz4K33z6ntuAINkKHyfyR8+ztr/HxhzccVaIAM1VzF8fCoj91X63EFGiBDpYF+etwJJ/365V+u880a7x8v0AAZKgz0xWt8fv/9Pr3ylW9/Z80LajquQANkqDDQH5tR2k4dXnqdo6bjCjRAhkoD/XJpe/8mbRtv9QbIVYWBvrbvljvvOGzVG2s+rkADZKgw0HP+fuMlv7zl3SVfq+FaaIEGyFBhoEc+9aEvPbFZ9ccVaIAMFQb6hD5HvLTkCy+O7nNC9ccVaIAMlV4HfedmPTY/7NTzTz10ZGHk3TUcV6ABMlT8TsLWPx2y8ZrLr7nxIX+q6TIOgQbIUM1bvetBoAEyCDRAUAINEJRAAwQl0ABBCTRAUBUGetP/tXFNxxVogAwVBnr06NGHDRi8/3f3GTTo+JqOK9AAGSp/iePfD1/Utl34jZNrOq5AA2SoPND9niwvjwyo6bgCDZCh8kCvOLG83L5iTccVaIAMlQf6Cxtc/PDU+y8cuFNNxxVogAyVB/rVHQsl275Y03EFGiBDNddBv3LXH++oLc8CDZCpqjeqzHpmbq3HFWiADFUE+hfDCoUn39p1Vk3HFWiADJUH+oLeB1/T68k3Nz+ypuMKNECGygO90SXF4gpPFlsG13RcgQbIUHmgV3i2HOjnV6jpuAINkKHyQI+4qhzo6zas6bgCnY+/v5d6AqBuKg/0iX1Our/37y5Y88SajivQebhi0NqrbH1/6imAOqk80AuO6l0oFFY4YkFNxxXoHPx0i0eLrdcPeDD1HEB9VHMd9MyJN907s8bjCnT9zV2j/Pahq7+QehCgPnyiSuP4n5Hl5e01E88B1EnlgX790E8O3aikpuMKdP1NWfwhN39bO/EcQJ1UcTe7gQcdW1bTcQW6/hb0+2tpufirqQcB6qPyQK/8Sj2OK9A5uHzwzXNnnN9vauo5gPqoPNADart8YwmBzsMd2/RZ/SvTUk8B1EnlgT7ysnocV6DzsTD1AED9VB7o8wbudMzxJTUdV6ABMlTxobEDl6jpuAINkMF10ABBCTRAUBUG+tRHiqcuVdNxBRogQ4WBXuuK4lpL1XRcgQbI4CUOgKCqDvQbm9d0XIEGyFBFoG84dJ999tl78zVqOq5AA2SoPNA/77llr1GDC5+/vabjCjRAhsoDPfSKYv/Xi5M+PqWm4wo0QIYqPtX7meK6LxeLt36mpuMKdGKv3/TH6alnAJatird631/c9O5i8dW+NR1XoJNqPbHfrl/pf/j81HMAy1J5oPcd9sLoze6aesjgmo4r0Emds93fisUZ/zom9RzAslTxkVdfeub1zQuFFa+r6bgCndRHyqf9b6vPSz0IsAzVXQc9f9Jdb9Z2XIFOaV7v1vL6kRfSzgEsk3cSNqPWvv8oLQtXeyf1JMAyVHqzpA+p6bgCndQ+p5W2v94+9RzAslR6s6QPqem4Ap3U9CHfvPWuI9bz8bIQmpc4mtN7p39x5xO8wAGxVRPo96b999Pv13hcgQbIUHmg39xvxUKhsPK3/lHTcQUaIEPlgd5tg9NvvOsPP1x175qOK9AAGSoP9ErPlJeb+tR0XIEGyFB5oNdcVF6ecRUHQK4qD/QBV5aXE75X03EFGiBD5YE+Y42dvn/ymE8OPKmm96oINECGygO9al3eqyLQABm8UQUgqMoDff3iZf5/1HRcgQbIUHmge+xVutPo5M1Xrem4Ag2QofJA37/F2tfN/eHye71W03EFGiBDFa9BL7xwjTWH3FbjcQUaIEMVgW69bJ1VPtpS43EFGiBD5YF+7FOrXzp7zPIH1PaZVwINkKHyQPfc7dW27cObr1nTcQUaIEPlgV7yad7zT6npuAINkKGqN6rMemZurccVaIAMVQT6F8MKhSff2nVWTccVaIAMlQf6gt4HX9PryTc3P7Km4wo0QIbKA73RJcXiCk8WWwbXdFyBBshQeaBXeLYc6OdXqOm4Ag2QofJAj7iqHOjrNqzpuAINkKHyQJ/Y56T7e//ugjVPrOm4Ag2QofJALziqd6FQWOGIBTUdV6ABMlRzHfTMiTfdO7PG4wo0QAafqAIQlEADBCXQAEEJNEBQbpYEEJSbJQEE5WZJAEG5WRJAUG6WBBCUmyUBBOVmSQBBuVkSQFBulgQQVBWBfv7Ots2Vz9Z2XIEGyFB5oCf13aNtu32fSTUdV6ABMlQe6O2+Oa9tu+hb29V0XIEGyFB5oPtMLi8P963puAINkKHyQK93fXm5fEBNxxVogAyVB/qH/S78ryn3n77yMTUdV6ABMlQe6HmjVygUCn0Oz7jj6MzJEyY8OrvDbws0QIaqroN+4JaHMq6DvmFUoaTnjhM72EGgATLk84kqV/TY++oHpkyZdOmuPW9ofw+BBshQeaBfP/STQzcqWcbOI05a+mjMyPb3EGiADJUH+gsDDzq2bBk79/rglY2W3u3vIdAAGSoP9MqvZO88+Kylj84Y2v4eAg2QofJAD+jEbezO7XV0y/QZM166e/Ty49rfQ6ABMlQe6CMv68Te4waVr+IoDL+qgx0EGiBD5YE+b+BOxxxfsszdW6fdNH78Lc91+H2BBshQeaD7DVyipuMKNECGfK6D/l/b9fzwn/5rp6V6/rS65wNoGlUH+o3NO/VjF4398J/m3HPXEiv9spPHBWhWVQT6hkP32WefvTdfo6bjeokDIEPlgf55zy17jRpc+Pztnfqxea+3/3WBBshQeaCHXlHs/3px0senLGPnhWdvNeSA8qcWtnTw2rVAA2SoPNArPFNc9+Vi8dbPLGPn05fb85hNVy39HVugAapUxWV29xc3vbtYfHVZH3k19MfF4oIxK94j0ABVqzzQ+w57YfRmd009ZPAydl7p3tJ2zKqPCDRAtaq43eiXnnl980JhxeuWsfOIs0vb1gPXnizQAFWq7jro+ZPuenNZO5/Ze+w7bcvC7/bZQ6ABqpPPOwkXHrdy+RqO4pUDBRqgOhUG+tRHiqcutczd5y9avM55rP3vC3T3Mf8fqSeAJlVhoNe6orjWUjUdV6C7iye/2Gf1DcYtSj0GNKPKX+KY0Ykb9mcT6G7imf7j5hX/8qmx2XsC9VbFG1X+ux7HFehu4ltnlLZvr7HMfxQGclHFh8YeV4/jCnQ3sdni/0A+f1viOaAZVR7oczfe6vDsT1TJItDdxMjHy8vOnbs3FlBPlQd67fV8okoT+c7Jpe0ba76dehBoQnl/okpHBLqbeHHdn7676MEt/yP1HNCM8v5ElY4IdHfx/J6r9P3oVa2px4Bm5BNVyNI6J/UE0KTy/kSVjgg0QIZ8PlElm0ADZMjnE1WyCTRAhnw+USWbQANkyOcTVbIJNECGCgN94dud+kSVbAINkKHCQBdW2PPmhdmfqJJNoAEyVBjoaScMKQwYM7X24wo0QIbKX4O+/9C1CltdWOutGQQaIEM1b/We/6e9Vlxhz5trOq5AA2So8l4c7/56QG13TxJogAxVBXr2tbuuuPI3azquQANkqDzQi+7+5io9PnP5e7UdV6ABMlQa6P/5/sDCkP94oebjCjRAhgoDPbKwyr9NrMdxBRogQ4WB3vGq2fU5rkADZPCRVwBBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAUz9vPf5u6hGgkQg09TJtx7U+tuqBf089BjQOgaZO3hp40aLie0dusyj1INAwBJo6Of3b5WXU7YnngMYh0NTJ164rL2N/kngOaBwCTZ1889fl5YhzE88BjUOgqZMrdmxt284a1Nn/ooAsAk2dLPiXLz/05t1bjU49BzQOgaZe5p358XW2vbY19RjQOAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoEng7YuP/tkLqYeA8ASarnfzgAN/dkS/C1OPAdEJNF3ujXUebttOHzg59SAQnEDT5X61f3n58bGJ54DoBJoud/KJ5eU3+ySeA6ITaLrcpV8vL/4GDRkEmi735joPtW1fWu/R1INAcAJN17t5wDfPPaLfxanHgOgEmgTevvioc19MPQSEJ9AAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFC5BXrm5AkTHp3d4bcFGiBDToG+YVShpOeOEzvYQaABMuQT6Ct67H31A1OmTLp01543tL+HQANkyCfQI05a+mjMyPb3EGiADPkEutcHr2y09G5/D4EGyJBPoAeftfTRGUPb30OgATLkE+hzex3dMn3GjJfuHr38uPb3EGiADDldxTFuUPkqjsLwqzrYQaABMuR1HXTrtJvGj7/luQ6/L9AAGbyTECCovAO9Xc//88d3lhJogAx5B/qisR/+U8saS/U4q6OfAKDMSxyE1nr3uVdOTz0EJJJ3oN9/of2vCzSd8cLWnzzqG/38v1s0qbwD3dLBzwk0ndC61U/btq9+9MbUg0ASAk1gj45oLS2/3TX1IJBETu8k/MBogaZ6f/xyeZm6aeI5II18At1j+RWW6CXQVG/SqPJyx2cSzwFp5BPoH208a8kjL3FQg/mD7mrbLtjx4tSDQBL5BHrhDnsueSTQ1GLigMOuvWDkXgtSzwFJ5PSPhK+d/fLiB49s3f4OAk2nvH36voffmnoISMQbVQCCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmgaxkt/Sz0B1JdA0yAuW3dQ/+E3p54C6kmgaQwXfKztP+WWQQpNIxFoGsLCftNKy62fTD0I1JFA0xBe2LC8zF9hUdo5oJ4EmobwyrrlZfaKrYkHgToSaBrDRveWtpfvknoOqCOBpjHctO7V77xxXr/HUs8BdSTQNIiHPr96v72eTj0F1JNAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAA/LfI14AABRQSURBVAQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0DSfuY89ODP1DNAJAk3TuXq9LbZZ64SFqceATAJNs/nDsLb/6F/beWzqOSCTQNNsRt1e2r6x2uzUg0AWgabZ9H2vvIz8S+I5IJNA02zWea28DHou8RyQSaBpNvudXtreM6w19SCQRaBpNi+uf8wjT5zT7+7Uc0AmgabpvH3MlhsfMC31FJBNoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBhg79/bvr9/74da2px6BpCTR05K3BP3h53p+3OD71HDQtgYaO/Gh0afvWWq+mHoRmJdDQke1bystXrk87Bs1LoKEj295XXva+LvEcNC2Bho4ccWJpO2fdZ1MPQrMSaOjIiwN+uaj4yv93YOo5aFoCDR2auuMqG65+ytzUY9C0BBqWYdaLi1KPQBMTaICgBBogKIEGCEqgAYISaICgBBogKIEGCEqgAYISaICgBBogKIEGCEqgAYISaICgBBogKIEGCEqgAYISaICgBBogKIEGCEqgAYISaICgBBogKIGGunj/6XmpR6DhCDTUwav79B3W5+C3Uo9BgxFoqN3sYSe9X5z1va0WpB6ExiLQULtL9igv2/8h8Rw0GIGG2n37kvJy2vGJ56DBCDTUbvTPy8sJJ6Udg0Yj0FC73/5La9t2/mYtqQehsQg01G7hDntMnfvILl9NPQcNRqChDt4/bfiKm57nIg7qS6ABghJogKAEGiAogQYISqABghJogKAEGrpW6+RrJsxJPQTdg0BDl3r205vst/2gW1KPQbcg0NCV5n30/NZi8YF+T6QehO5AoKEr3fIv5eXUoxLPQbcg0NCVzjuyvNz6xcRz0C0INHSlK/b78ALLJNDQlV5d+4W27fxtfpN6ELoDgYYudcl650y8Zst9WlPPQXcg0NC1/vKd7ff9Y+oh6B4EGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogYao5vzisB/ek3oIUso10G89/l5H3xJoyDBl8F4XnbHxPvNTz0E6+QT6O9PaNrO/Xij0PHZh+3sINCxb6+ZXtm3nf/6c1IOQTj6BLrS0bcb2/cltJ654Xvt7CDQs2183Ki/3fzLxHCSUY6A3Kv1f/tM2an8PgYZlu+dz5eXlgYnnIKEcA933wbbNfb3b30OgYdme3mBRabln29SDkE6Ogd669Llrv1m//T0EGjJsd2bbZuY2v0w9B+nkFOjdjjnxzD22KRanDD+g/T0EGjK89IlPn/C9gUf7/O8mlk+gd91hy6H9+6xeLH552PT29xBoyLLoP0/5+dTUQ5BSntdBLyoWn5rbwfcEGiCDdxICBJV3oLfr+eE/vf27pVYcV93zATSNvAN90dgP/+nxvZZa/mfVPR9A0/ASB0BQuQV65uQJEx6d3eG3BRogQ06BvmFUoaTnjhM72EGgATLkE+greux99QNTpky6dNeeN7S/h0ADZMgn0CNOWvpozMj29xBoqJ+5F+y730UdvemA7iufQPf64JWNFjdLgrxNH7bXteO/OuLV1HNQb/kEevBZSx+dMbT9PQQa6ubLp5e2J++Veg7qLZ9An9vr6JbpM2a8dPfo5Tt4Q4pAQ73M7Vu+XurdvgtST0Kd5XQVx7hB5as4CsOv6mAHgYZ6eXOdxesa76Sdg7rL6zro1mk3jR9/y3Mdfl+goV4WrlF+9Xn62u5M2mi8kxC6vTFfmV0szvrXH6Weg3oTaOj25h20/kH/NvCQ+annoN4EGhrAE7++7MnUM1B/Ag0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQ0EzeOGzYel98IPUUdJJAQxOZvv5xT796zfrXpJ6DzhFoaCLfOrm0ndJvXupB6BSBhiYy5Ony8vHJieegcwQamsjAV8rLtvclnoPOEWhoIl+6qrR9e/V/pB6EThFoaCL3rXdXsTh9xzGp56BzBBqayZ0jNtx0jdMWph6DzhFoaC4vTp2fegQ6S6ABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJooF0Pjj3g9L+lHqLJCTTQnqM/cvqVR/S7KfUYzU2ggXbcOHJW2/ahfjNSD9LUBBpox9cvKy+7/T7xHM1NoIF27HxneRl9YeI5mptAA+341vnl5XM3J56juQk00I77Nnihbfvbj7yfepCmJtBAey7pd8BxOw9/LPUYzU2ggXa9dtnpN8xLPUSTE2iAoAQaICiBBmr0m52G7Xh1a+opGpFAA7U56JM3P33rp/ZLPUYjEmigJpOGz2nbzt3sztSDNCCBBmpy4gnl5YwxiedoRAIN1GTM2eXlokMTz9GIBBqoyeW7l5d9L048RyMSaKAms4f8bGFx0YUbzkw9SAMSaKA2z+08cIcNPjMt9RiNSKCBWr345xdSj9CYBBogKIEGCEqgAYISaICgBBogKIEGCEqggS7z2636DjnqndRTdB8CDXSV4z4+4b1nDx8+I/Uc3YZAA11k+jpvl5aDTkk9SLch0EAXuW6v8nLHTonn6D4EGugiV+1fXv68feI5ug+BBrrI1A3mlZYfHZN6kG5DoIGusvcebxQX/XLA9NRzdBsCDXSVOcetMmy1z01NPUb3IdBA11nwtGvsKiDQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQQECvj/ncv/5sbuopUhNoIJ4H+v/wnv/ca9O3Us+RmEAD8Wx8U2l75OjUcyQm0EA4z2xYXp7dMOkU6Qk0EM7kT5SXGasmniM1gQbCeWe1WaXl7lGpB0lMoIF4Dtz/vWLxxc2uST1HYgINxDP739bd8wv9zk09RmoCDUT03O9va/aL7AQaICyBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBrq1mSd8bvsxb6aeIh8CDXRnzw867J6JP+j/eOo5ciHQQHf21bNL26u2TT1HLgQa6MZa+7xbWhau/k7qSfIg0EA3Nm+FxeuGL6adIx8CDXRnQx4rbV9dc37qQfIg0EB3dv6ol4vFv+9yfOo5ciHQQHfWesY6O31hnR8sTD1HLgQa6N7euuv2v6WeIScCDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk00DTeeW5R6hEqItBAk3ho6zWGrH56d7pxtEADzWFK/98Wiy/tfHjqOSog0EBz+Pp5pe3MtV5PPUjnCTTQHDb5a3n5wq2J56iAQAPNYfNHy8tn70o8RwUEGmgOR/ywtH1pzZmpB+k8gQaaw2sbHPfirJuHn5d6jgoINNAk3vj2hitvd0undr3xa58+6OGcx+kEgQb4v1q/scXVky7Y4JzUcwg0wD+5ccu5bdvX+j+fehCBBvi/vnVxeTnk4sRzCDTAP9nzd+XluNMTzyHQAP/k+LHlZec/Jp5DoAH+yfP97i4WWy8cNjf1IAIN8E8mDN3maxtv+3TqMQQa4P9n7qTrHmlNPYRAA4Ql0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAA1TvyWN3G31/Xk8u0ABVGzfg1D+dN3hMTs8u0ADVemGdl9q2M4dPyOfpBRqgWhd+u7ycc0Q+Ty/QANU6+cTycu2++Ty9QANU6+qvlJfj/j2fpxdogGrNHPiHtu1/rfNsPk8v0ABVm/zRfzn8S+vfnNOzCzRA9ebfccF/zsrryQUaICiBBghKoAG6xPt/ebnCnxBogC7w/g9WHTlw8/sq+hmBBugC++/x92Lxxv5TKvkZgQbI3/P955SWn32zkh8SaID83bhbeXn8Y5X8kEAD5O+2ncrL/aMq+SGBBsjfP9Z8sbQcdlwlPyTQAF3g/CHXv/7XQ4e+XcnPCDRAV7hnp/6bfH9GRT8i0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEFTegZ73evtfF2iADPkEeuHZWw054NnSo5YOfk6gATLkE+jTl9vzmE1Xvb0o0ABVyyfQQ39cLC4Ys+I9Ag1QtXwCvdK9pe2YVR8RaIBq5RPoEWeXtq0Hrj1ZoAGqlE+gz+w99p22ZeF3++wh0ADVyekqjuNWLl/DUbxyoEADVCev66DnL1q8znms/e8LNEAG7yQECCrvQG/X88N/eminpZY/p7rnA2gaeQf6orEf/tOsu5Yaem91zwfQNFK9xLHNg/V9PoCGk1ugZ06eMOHR2R1+W6ABMuQU6BtGFUp67jixgx0EGiBDPoG+osfeVz8wZcqkS3fteUP7ewg0QIac3up90tJHY0a2v4dAA2TIJ9C9Pnhlo6V3+3sINECGfAI9+Kylj84Y2v4eAg2QIZ9An9vr6JbpM2a8dPfo5ce1v4dAA2TI6SqOcYPKV3EUhl/VwQ4CDZAhr+ugW6fdNH78Lc91+H2BBsjgnYQAQQk0QFACDRCUQAMEJdAAQQk0QFDJAv3DX5TsvH+j2W/H1BPU3T67pJ6g7r76pdQT1N1uu6eeoO6+tEfqCepul7N+UZmBiQL9q2+XHLTcxo1mSO/UE9TdoL6pJ6i7dVdPPUHdrb1O6gnqbrX1Uk9Qd313+XZlvjczTaAXe3/FPJ41qadGpJ6g7u7cOfUEdXfpt1JPUHcnnpx6gro78PLUE9TdZyfk9cwC3TkC3R0IdHcg0BUQ6M4R6O5AoLsDga6AQHeOQHcHAt0dCHQFBLpzBLo7EOjuQKArINCdI9DdgUB3BwJdAYHuHIHuDgS6OxDoCgh05wh0dyDQ3YFAVyCXQM9bNY9nTerZTVNPUHctX0w9Qd1d+Z3UE9TdKaennqDuDr4m9QR1t/OkvJ45l0AX38rlWZNqvF9p0TupJ6i7ee+mnqDuZs9JPUHdzZyfeoK6e7s1r2fOJ9AA1EygAYISaICgBBogKIEGCEqgAYISaICgBBogKIEGCEqgAYISaICgBBogqDwC/fMhvUdclcPzprNroaRhbpW28Pge55YfNM6ZWvorNc6ZWnjOJn1GnLmw2EBn6YNfqXHO0pwfDOq94RkLirmdpRwC/YteZ008qcef6v/E6eywW0ubaanHqJPXdth4+XLNGudMffArNc6ZOq73mX8+ZbmzG+ksffArNc5Z+lq/X9/74+VOye8s1T/QrRse1bbde1TdnzihLY9JPUE9nb3nrBVKNWugM7X0V2qcMzV/5bFt2z23aqCz9MGv1Dhn6R+rX9m23XOL/M5S/QM9rdDStr26MLPuz5zO0BNTT1BPLxeL5Zo10Jla+is1zpla9Ezpdt3HDGmgs/TBr9Q4Z2mxr22V31mqf6BvKkxv2z5QmFz3Z06n/5mpJ6izcs0a60wtDnRjnakFIw9osLNU/pUa6izNef2Sla7L7yzVP9DjCzPatlMKuX1KVwJ99tx65aHHNdBnW5Rr1lhnanGgG+tMjVn56QY7S+VfqaHO0g6FNa7N8X9LAt0Ji1bf+vf3n9nn66nnqJ9GDXRjnamxK9zSaGep/Cs11Fn6yx1je1/cnQJ9S+Gltu2kwqN1f+bEflJonM8lLNessc7U4r9BlzXGmVp08Cr3FBvrLC35lRZrjLNUctpK7+V2luof6GcLd7dtL+05u+7PnNhthcdTj1A35Zo11pn6UKAb40wdttbDpaWRztKSX2mxRjhLr1w1q1j6x5wncztLOVwHPWx022bXz9X/iZN56itT27Yn9nwv9SB1s7hmDXWmyr9SA52pK1daErPGOUtLf6XGOUuTC9e0bU9ebk5uZymHQF+5/Bl/HrNcS/2fOJm5Q0f8/v4zVjw29Rx18khLS6/RLS3vN9CZWvorNc6ZmrP+V0tv5miZ1zhn6YNfqXHOUnGXNcfd+7M+h+RXvTze6n3R4F6bXJ/D86bzwr7r9trovIWpx6iTrctvtC280EBn6oNfqWHO1GOLf6PC641zlv73V2qYs1ScdfSA3sNOKF2PktNZcrMkgKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogaYx7V5os/bnJpYet161/Wp9Pnr826lnggoJNI1p941aWlqu3X65h9oe799jn+v++MPVhr+eeiiojEDTmHb/WGn7/sCvF4uXFX5RevzEKoekHQkqJdA0psWBLn5x62Jxi1GLv/To7ITzQBUEmsa0JNAf3aM4o8fxiWeBKgk0jWn3jy1YsOCVsYU7ik8Wfp16GKiOQNOYyldxFNa4rFh8qnBV6mGgOgJNY9p9+MMPP/zX+W2P3l3uhNTDQHUEmsa05DXokk8Mmltef/+fqYaB6gg0jelDgb6m8KPSMnWNg5NNA1URaBrThwJdPLjwhSuuP27VbWakGweqIdA0pg8HuvibHVbrO/JMl0HT3Qg0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFD/D75WXN2CaoFnAAAAAElFTkSuQmCC" />
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```r
```r
plotPCA(myeloid_sce, colour_by=\organ\, ncomponents=6)
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<img src="data:image/png;base64," />
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```r
```r
plotPCA(myeloid_sce, colour_by=\anno_lvl_2\, ncomponents=6)
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<img src="data:image/png;base64," />
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```r
plotPCA(myeloid_sce, colour_by="anno_lvl_2", text_by="anno_lvl_2")
plotPCA(myeloid_sce, colour_by="organ", text_by="anno_lvl_2")
Minimize obvious technical effects (3GEX/5GEX) using linear regression (following procedure from OSCA)
library(batchelor)
myeloid_sce_3gex <- myeloid_sce[,myeloid_sce$method=="3GEX"]
myeloid_sce_5gex <- myeloid_sce[,myeloid_sce$method=="5GEX"]
set.seed(10001)
residuals <- regressBatches(myeloid_sce_3gex, myeloid_sce_5gex, d=30,
assay.type = "logcounts",
correct.all=TRUE,
BSPARAM=BiocSingular::RandomParam())
assay(myeloid_sce, "scaled_logcounts") <- as.matrix(assay(residuals[,colnames(myeloid_sce)], "corrected"))
Model 1 - Normal MOFA / organs as views
Make MOFA object. Use - Organ as view - Celltypes as groups
## Split into one matrix x organ
org_ixs <- split(colnames(myeloid_sce), myeloid_sce$organ)
data <- lapply(org_ixs, function(i) assay(myeloid_sce[,i], "scaled_logcounts"))
data <- lapply(data, as.matrix)
## Collapse measurements from the same donor/library prep
new_sample <- paste(myeloid_sce$donor, myeloid_sce$method,myeloid_sce$anno_lvl_2, sep="-")
newsample_ixs <- split(new_sample, myeloid_sce$organ)
for (o in names(data)){
colnames(data[[o]]) <- newsample_ixs[[o]]
}
## Fill missing values
sample_names_unique <- unique(new_sample)
for (o in names(data)){
for (s in sample_names_unique){
if (!s %in% colnames(data[[o]])) {
m <- matrix(NA, nrow=nrow(data[[o]]))
colnames(m) <- s
data[[o]] <- cbind(data[[o]], m)
}
}
data[[o]] <- data[[o]][,sample_names_unique]
}
## Vector for group assignment
groups <- sapply(strsplit(sample_names_unique, "-"), function(x) x[3])
myeloid_mofa <- create_mofa_from_matrix(data, groups = groups)There are duplicated features names across different views. We will add the suffix *_view* only for those features
Example: if you have both TP53 in mRNA and mutation data it will be renamed to TP53_mRNA, TP53_mutationmyeloid_mofaUntrained MOFA model with the following characteristics:
Number of views: 9
Views names: BM GU KI LI MLN SK SP TH YS
Number of features (per view): 4256 4256 4256 4256 4256 4256 4256 4256 4256
Number of groups: 19
Groups names: Basophil CD14_monocyte CLP DC_progen DC1 DC2 DC3 Eosinophil_MOP ERY_MAC GMP HSC_MPP MAC MAST_cell Mono_Mac MPP_MYE myelocyte neutrophil promonocyte Promyelocyte
Number of samples (per group): 20 25 21 11 22 29 24 19 23 17 19 22 20 25 18 8 21 23 19
Regular MOFA
myeloid_mofaUntrained MOFA model with the following characteristics:
Number of views: 9
Views names: BM GU KI LI MLN SK SP TH YS
Number of features (per view): 4256 4256 4256 4256 4256 4256 4256 4256 4256
Number of groups: 19
Groups names: Basophil CD14_monocyte CLP DC_progen DC1 DC2 DC3 Eosinophil_MOP ERY_MAC GMP HSC_MPP MAC MAST_cell Mono_Mac MPP_MYE myelocyte neutrophil promonocyte Promyelocyte
Number of samples (per group): 20 25 21 11 22 29 24 19 23 17 19 22 20 25 18 8 21 23 19
Prepare 4 training
data_opts <- get_default_data_options(myeloid_mofa)
data_opts$center_groups <- FALSE
model_opts <- get_default_model_options(myeloid_mofa)
model_opts$num_factors <- 20
train_opts <- get_default_training_options(myeloid_mofa)
train_opts$seed <- 2020
train_opts$convergence_mode <- "fast" # use "fast" for faster training
train_opts$stochastic <- FALSE
mefisto_opts <- get_default_mefisto_options(myeloid_mofa)
mefisto_opts$warping <- FALSE
# mefisto_opts$sparseGP <- TRUE
myeloid_mofa <- prepare_mofa(
object = myeloid_mofa,
data_options = data_opts,
model_options = model_opts,
training_options = train_opts,
mefisto_options = mefisto_opts
) Some group(s) have less than 10 samples, MOFA will have little power to learn meaningful factors for these group(s)...
# Multi-group mode requested.
This is an advanced option, if this is the first time that you are running MOFA, we suggest that you try do some exploration first without specifying groups. Two important remarks:
- The aim of the multi-group framework is to identify the sources of variability *within* the groups. If your aim is to find a factor that 'separates' the groups, you DO NOT want to use the multi-group framework. Please see the FAQ in (https://biofam.github.io/MOFA2)
- It is important to account for the group effect before selecting highly variable features (HVFs). We suggest that either you calculate HVFs per group and then take the union, or regress out the group effect before HVF selection
Checking data options...
Checking training options...
Checking model options...Train
outfile <- "/nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model.hdf5"
myeloid_mofa_trained <- run_mofa(myeloid_mofa, outfile = outfile)Connecting to the mofapy2 python package using reticulate (use_basilisk = FALSE)...
Please make sure to manually specify the right python binary when loading R with reticulate::use_python(..., force=TRUE) or the right conda environment with reticulate::use_condaenv(..., force=TRUE)
If you prefer to let us automatically install a conda environment with 'mofapy2' installed using the 'basilisk' package, please use the argument 'use_basilisk = TRUE'
#########################################################
### __ __ ____ ______ ###
### | \/ |/ __ \| ____/\ _ ###
### | \ / | | | | |__ / \ _| |_ ###
### | |\/| | | | | __/ /\ \_ _| ###
### | | | | |__| | | / ____ \|_| ###
### |_| |_|\____/|_|/_/ \_\ ###
### ###
#########################################################
Successfully loaded view='BM' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='BM' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='BM' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='BM' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='BM' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='BM' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='BM' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='BM' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='BM' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='BM' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='BM' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='BM' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='BM' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='BM' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='BM' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='BM' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='BM' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='BM' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='BM' group='Promyelocyte' with N=19 samples and D=4256 features...
Successfully loaded view='GU' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='GU' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='GU' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='GU' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='GU' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='GU' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='GU' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='GU' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='GU' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='GU' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='GU' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='GU' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='GU' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='GU' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='GU' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='GU' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='GU' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='GU' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='GU' group='Promyelocyte' with N=19 samples and D=4256 features...
Successfully loaded view='KI' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='KI' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='KI' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='KI' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='KI' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='KI' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='KI' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='KI' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='KI' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='KI' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='KI' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='KI' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='KI' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='KI' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='KI' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='KI' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='KI' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='KI' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='KI' group='Promyelocyte' with N=19 samples and D=4256 features...
Successfully loaded view='LI' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='LI' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='LI' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='LI' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='LI' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='LI' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='LI' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='LI' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='LI' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='LI' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='LI' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='LI' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='LI' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='LI' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='LI' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='LI' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='LI' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='LI' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='LI' group='Promyelocyte' with N=19 samples and D=4256 features...
Successfully loaded view='MLN' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='MLN' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='MLN' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='MLN' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='MLN' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='MLN' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='MLN' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='MLN' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='MLN' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='MLN' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='MLN' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='MLN' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='MLN' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='MLN' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='MLN' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='MLN' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='MLN' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='MLN' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='MLN' group='Promyelocyte' with N=19 samples and D=4256 features...
Successfully loaded view='SK' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='SK' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='SK' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='SK' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='SK' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='SK' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='SK' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='SK' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='SK' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='SK' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='SK' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='SK' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='SK' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='SK' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='SK' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='SK' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='SK' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='SK' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='SK' group='Promyelocyte' with N=19 samples and D=4256 features...
Successfully loaded view='SP' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='SP' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='SP' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='SP' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='SP' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='SP' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='SP' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='SP' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='SP' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='SP' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='SP' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='SP' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='SP' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='SP' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='SP' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='SP' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='SP' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='SP' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='SP' group='Promyelocyte' with N=19 samples and D=4256 features...
Successfully loaded view='TH' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='TH' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='TH' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='TH' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='TH' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='TH' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='TH' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='TH' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='TH' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='TH' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='TH' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='TH' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='TH' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='TH' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='TH' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='TH' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='TH' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='TH' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='TH' group='Promyelocyte' with N=19 samples and D=4256 features...
Successfully loaded view='YS' group='Basophil' with N=20 samples and D=4256 features...
Successfully loaded view='YS' group='CD14_monocyte' with N=25 samples and D=4256 features...
Successfully loaded view='YS' group='CLP' with N=21 samples and D=4256 features...
Successfully loaded view='YS' group='DC_progen' with N=11 samples and D=4256 features...
Successfully loaded view='YS' group='DC1' with N=22 samples and D=4256 features...
Successfully loaded view='YS' group='DC2' with N=29 samples and D=4256 features...
Successfully loaded view='YS' group='DC3' with N=24 samples and D=4256 features...
Successfully loaded view='YS' group='Eosinophil_MOP' with N=19 samples and D=4256 features...
Successfully loaded view='YS' group='ERY_MAC' with N=23 samples and D=4256 features...
Successfully loaded view='YS' group='GMP' with N=17 samples and D=4256 features...
Successfully loaded view='YS' group='HSC_MPP' with N=19 samples and D=4256 features...
Successfully loaded view='YS' group='MAC' with N=22 samples and D=4256 features...
Successfully loaded view='YS' group='MAST_cell' with N=20 samples and D=4256 features...
Successfully loaded view='YS' group='Mono_Mac' with N=25 samples and D=4256 features...
Successfully loaded view='YS' group='MPP_MYE' with N=18 samples and D=4256 features...
Successfully loaded view='YS' group='myelocyte' with N=8 samples and D=4256 features...
Successfully loaded view='YS' group='neutrophil' with N=21 samples and D=4256 features...
Successfully loaded view='YS' group='promonocyte' with N=23 samples and D=4256 features...
Successfully loaded view='YS' group='Promyelocyte' with N=19 samples and D=4256 features...
Model options:
- Automatic Relevance Determination prior on the factors: True
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (BM): gaussian
- View 1 (GU): gaussian
- View 2 (KI): gaussian
- View 3 (LI): gaussian
- View 4 (MLN): gaussian
- View 5 (SK): gaussian
- View 6 (SP): gaussian
- View 7 (TH): gaussian
- View 8 (YS): gaussian
Warning: some group(s) have less than 15 samples, MOFA won't be able to learn meaningful factors for these group(s)...
######################################
## Training the model with seed 2020 ##
######################################
ELBO before training: -49128692.21
Iteration 1: time=12.71, ELBO=-7398394.56, deltaELBO=41730297.651 (84.94078668%), Factors=20
Iteration 2: time=12.54, Factors=20
Iteration 3: time=12.53, Factors=20
Iteration 4: time=12.54, Factors=20
Iteration 5: time=12.52, Factors=20
Iteration 6: time=12.74, ELBO=-5625656.57, deltaELBO=1772737.990 (3.60835575%), Factors=20
Iteration 7: time=12.60, Factors=20
Iteration 8: time=12.53, Factors=20
Iteration 9: time=12.55, Factors=20
Iteration 10: time=12.42, Factors=20
Iteration 11: time=12.61, ELBO=-5397124.31, deltaELBO=228532.261 (0.46517066%), Factors=20
Iteration 12: time=12.46, Factors=20
Iteration 13: time=12.95, Factors=20
Iteration 14: time=12.55, Factors=20
Iteration 15: time=12.49, Factors=20
Iteration 16: time=12.75, ELBO=-5315593.98, deltaELBO=81530.327 (0.16595257%), Factors=20
Iteration 17: time=12.48, Factors=20
Iteration 18: time=12.57, Factors=20
Iteration 19: time=12.62, Factors=20
Iteration 20: time=12.66, Factors=20
Iteration 21: time=12.65, ELBO=-5279402.23, deltaELBO=36191.751 (0.07366724%), Factors=20
Iteration 22: time=12.62, Factors=20
Iteration 23: time=12.52, Factors=20
Iteration 24: time=12.50, Factors=20
Iteration 25: time=12.41, Factors=20
Iteration 26: time=12.66, ELBO=-5258330.27, deltaELBO=21071.959 (0.04289135%), Factors=20
Iteration 27: time=12.40, Factors=20
Iteration 28: time=12.43, Factors=20
Iteration 29: time=12.36, Factors=20
Iteration 30: time=12.26, Factors=20
Iteration 31: time=12.77, ELBO=-5243781.65, deltaELBO=14548.617 (0.02961328%), Factors=20
Iteration 32: time=12.56, Factors=20
Iteration 33: time=12.42, Factors=20
Iteration 34: time=12.42, Factors=20
Iteration 35: time=12.47, Factors=20
Iteration 36: time=12.59, ELBO=-5232879.82, deltaELBO=10901.832 (0.02219036%), Factors=20
Iteration 37: time=12.46, Factors=20
Iteration 38: time=12.43, Factors=20
Iteration 39: time=12.47, Factors=20
Iteration 40: time=12.35, Factors=20
Iteration 41: time=12.48, ELBO=-5224408.87, deltaELBO=8470.953 (0.01724238%), Factors=20
Iteration 42: time=12.26, Factors=20
Iteration 43: time=12.27, Factors=20
Iteration 44: time=12.14, Factors=20
Iteration 45: time=12.30, Factors=20
Iteration 46: time=12.42, ELBO=-5217440.75, deltaELBO=6968.123 (0.01418341%), Factors=20
Iteration 47: time=12.25, Factors=20
Iteration 48: time=12.20, Factors=20
Iteration 49: time=12.20, Factors=20
Iteration 50: time=12.19, Factors=20
Iteration 51: time=12.24, ELBO=-4951118.76, deltaELBO=266321.982 (0.54209052%), Factors=20
Iteration 52: time=12.23, Factors=20
Iteration 53: time=12.11, Factors=20
Iteration 54: time=12.14, Factors=20
Iteration 55: time=12.21, Factors=20
Iteration 56: time=12.40, ELBO=-4939172.08, deltaELBO=11946.680 (0.02431711%), Factors=20
Iteration 57: time=12.31, Factors=20
Iteration 58: time=12.43, Factors=20
Iteration 59: time=12.25, Factors=20
Iteration 60: time=12.22, Factors=20
Iteration 61: time=12.33, ELBO=-4933010.57, deltaELBO=6161.510 (0.01254157%), Factors=20
Iteration 62: time=12.29, Factors=20
Iteration 63: time=12.15, Factors=20
Iteration 64: time=12.28, Factors=20
Iteration 65: time=12.29, Factors=20
Iteration 66: time=12.41, ELBO=-4928410.81, deltaELBO=4599.764 (0.00936268%), Factors=20
Iteration 67: time=12.30, Factors=20
Iteration 68: time=12.33, Factors=20
Iteration 69: time=12.24, Factors=20
Iteration 70: time=12.28, Factors=20
Iteration 71: time=12.40, ELBO=-4924838.86, deltaELBO=3571.949 (0.00727060%), Factors=20
Iteration 72: time=12.30, Factors=20
Iteration 73: time=12.17, Factors=20
Iteration 74: time=12.26, Factors=20
Iteration 75: time=12.23, Factors=20
Iteration 76: time=12.43, ELBO=-4921827.88, deltaELBO=3010.982 (0.00612876%), Factors=20
Iteration 77: time=12.32, Factors=20
Iteration 78: time=12.30, Factors=20
Iteration 79: time=12.20, Factors=20
Iteration 80: time=12.21, Factors=20
Iteration 81: time=12.30, ELBO=-4919173.53, deltaELBO=2654.353 (0.00540286%), Factors=20
Iteration 82: time=12.21, Factors=20
Iteration 83: time=12.08, Factors=20
Iteration 84: time=12.13, Factors=20
Iteration 85: time=12.13, Factors=20
Iteration 86: time=12.52, ELBO=-4916845.89, deltaELBO=2327.638 (0.00473784%), Factors=20
Iteration 87: time=12.28, Factors=20
Iteration 88: time=12.15, Factors=20
Iteration 89: time=12.16, Factors=20
Iteration 90: time=12.11, Factors=20
Iteration 91: time=12.31, ELBO=-4914830.53, deltaELBO=2015.361 (0.00410221%), Factors=20
Iteration 92: time=12.18, Factors=20
Iteration 93: time=12.17, Factors=20
Iteration 94: time=12.18, Factors=20
Iteration 95: time=12.25, Factors=20
Iteration 96: time=12.58, ELBO=-4912938.39, deltaELBO=1892.135 (0.00385138%), Factors=20
Iteration 97: time=12.30, Factors=20
Iteration 98: time=12.18, Factors=20
Iteration 99: time=12.13, Factors=20
Iteration 100: time=12.09, Factors=20
Iteration 101: time=12.37, ELBO=-4911181.01, deltaELBO=1757.384 (0.00357710%), Factors=20
Iteration 102: time=12.27, Factors=20
Iteration 103: time=12.19, Factors=20
Iteration 104: time=12.24, Factors=20
Iteration 105: time=12.21, Factors=20
Iteration 106: time=12.33, ELBO=-4909534.18, deltaELBO=1646.828 (0.00335207%), Factors=20
Iteration 107: time=12.16, Factors=20
Iteration 108: time=12.18, Factors=20
Iteration 109: time=12.21, Factors=20
Iteration 110: time=12.24, Factors=20
Iteration 111: time=12.38, ELBO=-4908064.86, deltaELBO=1469.324 (0.00299076%), Factors=20
Iteration 112: time=12.20, Factors=20
Iteration 113: time=12.19, Factors=20
Iteration 114: time=12.29, Factors=20
Iteration 115: time=12.22, Factors=20
Iteration 116: time=12.41, ELBO=-4906707.71, deltaELBO=1357.145 (0.00276243%), Factors=20
Iteration 117: time=12.14, Factors=20
Iteration 118: time=12.18, Factors=20
Iteration 119: time=12.25, Factors=20
Iteration 120: time=12.20, Factors=20
Iteration 121: time=12.28, ELBO=-4905419.49, deltaELBO=1288.226 (0.00262215%), Factors=20
Iteration 122: time=12.18, Factors=20
Iteration 123: time=12.08, Factors=20
Iteration 124: time=12.14, Factors=20
Iteration 125: time=12.07, Factors=20
Iteration 126: time=12.29, ELBO=-4904242.20, deltaELBO=1177.290 (0.00239634%), Factors=20
Iteration 127: time=12.25, Factors=20
Iteration 128: time=12.30, Factors=20
Iteration 129: time=12.26, Factors=20
Iteration 130: time=12.38, Factors=20
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Converged!
#######################
## Training finished ##
#######################
Warning: Output file /nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model.hdf5 already exists, it will be replaced
Saving model in /nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model.hdf5...Factor(s) 1, 2, 3, 6, 10 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.Load trained model
myeloid_mofa_trained <- load_model(outfile)
Factor(s) 1, 2, 3, 6, 10 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.
Warning message:
In system("timedatectl", intern = TRUE) :
running command 'timedatectl' had status 1plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 1:10)
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 11:20)
Plot weights correlation across organs (are the same genes related to a factor)
Add some covariates
Factor 1
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 1)
Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
Factor 4
Factor 6
plot_factor(myeloid_mofa_trained, factors = 6, color_by = "method")plot_data_heatmap(myeloid_mofa_trained, view="BM", factor = 6)gs <- get_view_genes(myeloid_mofa_trained, view="BM", factor=6)
plot_view_genes(gs)
plot_view_genes(gs, filter_anno = c("DC3"))Factor 7
Factor 8
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 8)
plot_data_heatmap(myeloid_mofa_trained, view="BM", factor = 8, annotation_samples = "group")
plot_data_heatmap(myeloid_mofa_trained, view="LI", factor = 8, annotation_samples = "group")
plot_data_heatmap(myeloid_mofa_trained, view="SK", factor = 8, annotation_samples = "group")
# sapply(get_weights(myeloid_mofa_trained, factors = 8, as.data.frame = FALSE), function(v) v[,1]) Factor 9
Factor 11
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 11)
Factor 12
Factor 13
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 13)
Factor 14
Make signatures from latent factors
get_weights()Model 2 - Normal MOFA / only celltypes as groups
Make MOFA object. Use - Celltypes as groups
myeloid_mofa <- create_mofa_from_SingleCellExperiment(myeloid_sce, assay = "scaled_logcounts", groups = "anno_lvl_2")
myeloid_mofaUntrained MOFA model with the following characteristics:
Number of views: 1
Views names: scaled_logcounts
Number of features (per view): 4256
Number of groups: 19
Groups names: Basophil CD14_monocyte CLP DC_progen DC1 DC2 DC3 Eosinophil_MOP ERY_MAC GMP HSC_MPP MAC MAST_cell Mono_Mac MPP_MYE myelocyte neutrophil promonocyte Promyelocyte
Number of samples (per group): 62 110 66 17 62 105 96 59 60 43 54 44 59 60 49 17 71 97 52
Prepare 4 training
data_opts <- get_default_data_options(myeloid_mofa)
data_opts$center_groups <- FALSE
model_opts <- get_default_model_options(myeloid_mofa)
model_opts$num_factors <- 20
train_opts <- get_default_training_options(myeloid_mofa)
train_opts$seed <- 2020
train_opts$convergence_mode <- "medium" # use "fast" for faster training
train_opts$stochastic <- FALSE
mefisto_opts <- get_default_mefisto_options(myeloid_mofa)
mefisto_opts$warping <- FALSE
# mefisto_opts$sparseGP <- TRUE
myeloid_mofa <- prepare_mofa(
object = myeloid_mofa,
data_options = data_opts,
model_options = model_opts,
training_options = train_opts,
mefisto_options = mefisto_opts
)
# Multi-group mode requested.
This is an advanced option, if this is the first time that you are running MOFA, we suggest that you try do some exploration first without specifying groups. Two important remarks:
- The aim of the multi-group framework is to identify the sources of variability *within* the groups. If your aim is to find a factor that 'separates' the groups, you DO NOT want to use the multi-group framework. Please see the FAQ in (https://biofam.github.io/MOFA2)
- It is important to account for the group effect before selecting highly variable features (HVFs). We suggest that either you calculate HVFs per group and then take the union, or regress out the group effect before HVF selection
Checking data options...
Checking training options...
Checking model options...Train
outfile <- "/nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model_oneview.hdf5"
myeloid_mofa_trained <- run_mofa(myeloid_mofa, outfile = outfile)Warning: Output file /nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model_oneview.hdf5 already exists, it will be replaced
Connecting to the mofapy2 python package using reticulate (use_basilisk = FALSE)...
Please make sure to manually specify the right python binary when loading R with reticulate::use_python(..., force=TRUE) or the right conda environment with reticulate::use_condaenv(..., force=TRUE)
If you prefer to let us automatically install a conda environment with 'mofapy2' installed using the 'basilisk' package, please use the argument 'use_basilisk = TRUE'
#########################################################
### __ __ ____ ______ ###
### | \/ |/ __ \| ____/\ _ ###
### | \ / | | | | |__ / \ _| |_ ###
### | |\/| | | | | __/ /\ \_ _| ###
### | | | | |__| | | / ____ \|_| ###
### |_| |_|\____/|_|/_/ \_\ ###
### ###
#########################################################
Successfully loaded view='scaled_logcounts' group='Basophil' with N=62 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='CD14_monocyte' with N=110 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='CLP' with N=66 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='DC_progen' with N=17 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='DC1' with N=62 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='DC2' with N=105 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='DC3' with N=96 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='Eosinophil_MOP' with N=59 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='ERY_MAC' with N=60 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='GMP' with N=43 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='HSC_MPP' with N=54 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='MAC' with N=44 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='MAST_cell' with N=59 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='Mono_Mac' with N=60 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='MPP_MYE' with N=49 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='myelocyte' with N=17 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='neutrophil' with N=71 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='promonocyte' with N=97 samples and D=4256 features...
Successfully loaded view='scaled_logcounts' group='Promyelocyte' with N=52 samples and D=4256 features...
Model options:
- Automatic Relevance Determination prior on the factors: True
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (scaled_logcounts): gaussian
######################################
## Training the model with seed 2020 ##
######################################
ELBO before training: -64791262.34
Iteration 1: time=5.15, ELBO=-1940142.21, deltaELBO=62851120.124 (97.00554960%), Factors=20
Iteration 2: time=5.17, Factors=20
Iteration 3: time=5.07, Factors=20
Iteration 4: time=5.10, Factors=20
Iteration 5: time=5.04, Factors=20
Iteration 6: time=4.88, ELBO=7790.37, deltaELBO=1947932.588 (3.00647420%), Factors=20
Iteration 7: time=4.82, Factors=20
Iteration 8: time=4.76, Factors=20
Iteration 9: time=4.59, Factors=20
Iteration 10: time=4.64, Factors=20
Iteration 11: time=4.73, ELBO=111294.38, deltaELBO=103504.005 (0.15974994%), Factors=20
Iteration 12: time=4.66, Factors=20
Iteration 13: time=4.69, Factors=20
Iteration 14: time=4.80, Factors=20
Iteration 15: time=4.61, Factors=20
Iteration 16: time=4.70, ELBO=146662.93, deltaELBO=35368.552 (0.05458846%), Factors=20
Iteration 17: time=4.69, Factors=20
Iteration 18: time=4.60, Factors=20
Iteration 19: time=4.70, Factors=20
Iteration 20: time=4.69, Factors=20
Iteration 21: time=4.75, ELBO=156218.74, deltaELBO=9555.811 (0.01474861%), Factors=20
Iteration 22: time=4.67, Factors=20
Iteration 23: time=4.54, Factors=20
Iteration 24: time=4.72, Factors=20
Iteration 25: time=4.67, Factors=20
Iteration 26: time=4.80, ELBO=160586.75, deltaELBO=4368.004 (0.00674166%), Factors=20
Iteration 27: time=4.66, Factors=20
Iteration 28: time=4.71, Factors=20
Iteration 29: time=4.68, Factors=20
Iteration 30: time=4.61, Factors=20
Iteration 31: time=4.74, ELBO=162870.20, deltaELBO=2283.457 (0.00352433%), Factors=20
Iteration 32: time=4.75, Factors=20
Iteration 33: time=4.71, Factors=20
Iteration 34: time=4.70, Factors=20
Iteration 35: time=4.69, Factors=20
Iteration 36: time=4.77, ELBO=164216.73, deltaELBO=1346.527 (0.00207825%), Factors=20
Iteration 37: time=4.43, Factors=20
Iteration 38: time=4.72, Factors=20
Iteration 39: time=4.50, Factors=20
Iteration 40: time=4.59, Factors=20
Iteration 41: time=4.73, ELBO=165066.98, deltaELBO=850.245 (0.00131228%), Factors=20
Iteration 42: time=4.58, Factors=20
Iteration 43: time=4.70, Factors=20
Iteration 44: time=4.69, Factors=20
Iteration 45: time=4.71, Factors=20
Iteration 46: time=4.75, ELBO=165691.45, deltaELBO=624.477 (0.00096383%), Factors=20
Iteration 47: time=4.65, Factors=20
Iteration 48: time=4.67, Factors=20
Iteration 49: time=4.69, Factors=20
Iteration 50: time=4.70, Factors=20
Iteration 51: time=4.74, ELBO=213469.63, deltaELBO=47778.175 (0.07374170%), Factors=20
Iteration 52: time=4.66, Factors=20
Iteration 53: time=4.72, Factors=20
Iteration 54: time=4.71, Factors=20
Iteration 55: time=4.71, Factors=20
Iteration 56: time=4.71, ELBO=215587.54, deltaELBO=2117.909 (0.00326882%), Factors=20
Iteration 57: time=4.69, Factors=20
Iteration 58: time=4.61, Factors=20
Iteration 59: time=4.69, Factors=20
Iteration 60: time=4.60, Factors=20
Iteration 61: time=4.72, ELBO=216209.92, deltaELBO=622.384 (0.00096060%), Factors=20
Iteration 62: time=4.67, Factors=20
Iteration 63: time=4.70, Factors=20
Iteration 64: time=4.65, Factors=20
Iteration 65: time=4.65, Factors=20
Iteration 66: time=4.75, ELBO=216619.35, deltaELBO=409.428 (0.00063192%), Factors=20
Iteration 67: time=4.60, Factors=20
Iteration 68: time=4.50, Factors=20
Iteration 69: time=4.98, Factors=20
Iteration 70: time=4.47, Factors=20
Iteration 71: time=4.74, ELBO=216946.72, deltaELBO=327.372 (0.00050527%), Factors=20
Iteration 72: time=4.66, Factors=20
Iteration 73: time=4.52, Factors=20
Iteration 74: time=4.59, Factors=20
Iteration 75: time=4.70, Factors=20
Iteration 76: time=4.95, ELBO=217229.16, deltaELBO=282.439 (0.00043592%), Factors=20
Iteration 77: time=5.14, Factors=20
Iteration 78: time=4.82, Factors=20
Iteration 79: time=4.70, Factors=20
Iteration 80: time=4.68, Factors=20
Iteration 81: time=4.74, ELBO=217441.31, deltaELBO=212.147 (0.00032743%), Factors=20
Converged!
#######################
## Training finished ##
#######################
Warning: Output file /nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model_oneview.hdf5 already exists, it will be replaced
Saving model in /nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model_oneview.hdf5...2 factors were found to explain no variance and they were removed for downstream analysis. You can disable this option by setting load_model(..., remove_inactive_factors = FALSE)myeloid_mofa_trained <- load_model(outfile)
Factor(s) 1 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.Add some covariates
samples_metadata(myeloid_mofa_trained) <- samples_metadata(myeloid_mofa_trained) %>%
mutate(time=myeloid_sce[,match(samples_metadata(myeloid_mofa_trained)$sample, colnames(myeloid_sce))]$age,
method=myeloid_sce[,match(samples_metadata(myeloid_mofa_trained)$sample, colnames(myeloid_sce))]$method,
donor=myeloid_sce[,match(samples_metadata(myeloid_mofa_trained)$sample, colnames(myeloid_sce))]$donor,
organ=myeloid_sce[,match(samples_metadata(myeloid_mofa_trained)$sample, colnames(myeloid_sce))]$organ)
Plot by celltype
get_variance_explained(myeloid_mofa_trained, as.data.frame = TRUE)[[1]] %>%
ggplot(aes(factor, value)) + geom_col() +
coord_flip() +
facet_wrap(group~., ncol = 6, scales = "free_x")
get_variance_explained(myeloid_mofa_trained, as.data.frame = TRUE, views = "scaled_logcounts")[[1]] %>%
pivot_wider(id_cols=c(group), names_from=factor, values_from=value) %>%
column_to_rownames("group") %>%
as.matrix() %>%
t() %>%
cor() %>%
pheatmap::pheatmap()
for (f in colnames(myeloid_mofa_trained@expectations$W$scaled_logcounts)){
print(plot_factor(myeloid_mofa_trained, factors = f, group_by = "group", color_by = "organ", dot_size = 0.8, add_boxplot = TRUE, dodge = TRUE))
}
Insights so far:
- Factor1:
- Factor2:
- Factor3: MAST/Baso/Eo vs rest
- Factor4: DC1 signature, TH specific
- Factor5: MAST/Baso/Eo, low in BM
- Factor6: progenitors signal, correlated with age (progenitor maturation?)
- Factor7: differences within ERY_MAC, more expression of hemoglobin genes in liver and bone marrow than SK 8/9. Factors 8 and 9 look technical (heatshock proteins)
- CLP vs GMP signature (BM vs SP and LI)
- Factor 14: GU specific DC and Mono signature
plot_weights(myeloid_mofa_trained, factors = 4, nfeatures = 50, text_size = 6)
Warning message:
In system("timedatectl", intern = TRUE) :
running command 'timedatectl' had status 1
Go by celltype instead of factor
DC1
Explore by factor
plot_factor(myeloid_mofa_trained, factor = 3)
plot_weights(myeloid_mofa_trained, factor = 3, nfeatures = 20)
Find factors that discriminate between organs
Model 3 - MEFISTO
Add time as covariate to run MEFISTO
```r
## Vector for time assignment
times <- distinct(data.frame(age=myeloid_sce$age, new_sample)) %>%
column_to_rownames('new_sample') %>%
.[sample_names_unique,]
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Model options: - Automatic Relevance Determination prior on the factors: True - Automatic Relevance Determination prior on the weights: True - Spike-and-slab prior on the factors: False - Spike-and-slab prior on the weights: True Likelihoods: - View 0 (BM): gaussian - View 1 (GU): gaussian - View 2 (KI): gaussian - View 3 (LI): gaussian - View 4 (MLN): gaussian - View 5 (SK): gaussian - View 6 (SP): gaussian - View 7 (TH): gaussian - View 8 (YS): gaussian
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc2FtcGxlc19tZXRhZGF0YShteWVsb2lkX21vZmEpW1tcXHRpbWVcXF1dIDwtIHRpbWVzXG5cbm15ZWxvaWRfbW9mYSA8LSBzZXRfY292YXJpYXRlcyhteWVsb2lkX21vZmEsIGNvdmFyaWF0ZXMgPSBcXHRpbWVcXClcbm15ZWxvaWRfbW9mYVxuYGBgXG5gYGAifQ== -->
```r
```r
samples_metadata(myeloid_mofa)[[\time\]] <- times
myeloid_mofa <- set_covariates(myeloid_mofa, covariates = \time\)
myeloid_mofa
<!-- rnb-source-end -->
<!-- rnb-output-begin 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 -->
Untrained MEFISTO model with the following characteristics: Number of views: 9 Views names: BM GU KI LI MLN SK SP TH YS Number of features (per view): 6857 6857 6857 6857 6857 6857 6857 6857 6857 Number of groups: 19 Groups names: Basophil CD14_monocyte CLP DC_progen DC1 DC2 DC3 Eosinophil_MOP ERY_MAC GMP HSC_MPP MAC MAST_cell Mono_Mac MPP_MYE myelocyte neutrophil promonocyte Promyelocyte Number of samples (per group): 20 25 21 11 22 29 24 19 23 17 19 22 20 25 18 8 21 23 19 Number of covariates per sample: 1
<!-- rnb-output-end -->
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```r
```r
gg_input <- plot_data_overview(myeloid_mofa,
show_covariate = TRUE,
show_dimensions = TRUE)
gg_input
<!-- rnb-source-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- Keep groups that span multiple views -->
<!-- ```{r} -->
<!-- gr_samples <- split(samples_metadata(myeloid_mofa)$sample, samples_metadata(myeloid_mofa)$group) -->
<!-- all(is.na(data$BM[,gr_samples$Basophil])) -->
<!-- lapply(unique(samples_metadata(myeloid_mofa)[["group"]]), function(x) data$BM[]) -->
<!-- myeloid_mofa@data -->
<!-- subse(myeloid_mofa)[,samples_metadata(myeloid_mofa)[["group"]] == "Basophil"] -->
<!-- ``` -->
Prepare 4 training
<!-- rnb-text-end -->
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```r
```r
data_opts <- get_default_data_options(myeloid_mofa)
model_opts <- get_default_model_options(myeloid_mofa)
model_opts$num_factors <- 10
train_opts <- get_default_training_options(myeloid_mofa)
train_opts$seed <- 2020
train_opts$convergence_mode <- \fast\ # use \fast\ for faster training
mefisto_opts <- get_default_mefisto_options(myeloid_mofa)
mefisto_opts$warping <- FALSE
# mefisto_opts$sparseGP <- TRUE
myeloid_mofa <- prepare_mofa(
object = myeloid_mofa,
data_options = data_opts,
model_options = model_opts,
training_options = train_opts,
mefisto_options = mefisto_opts
)
<!-- rnb-source-end -->
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Multi-group mode requested.
This is an advanced option, if this is the first time that you are running MOFA, we suggest that you try do some exploration first without specifying groups. Two important remarks:
The aim of the multi-group framework is to identify the sources of variability within the groups. If your aim is to find a factor that ‘separates’ the groups, you DO NOT want to use the multi-group framework. Please see the FAQ in (https://biofam.github.io/MOFA2)
It is important to account for the group effect before selecting highly variable features (HVFs). We suggest that either you calculate HVFs per group and then take the union, or regress out the group effect before HVF selection Checking data options… Checking training options… Stochastic inference activated. Note that this is only recommended if you have a very large sample size (>1e4) and access to a GPU No stochastic options specified, using default… Checking model options… Checking inference options for mefisto covariates…
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## Train
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxub3V0ZmlsZSA8LSBcIi9uZnMvdGVhbTIwNS9lZDYvZGF0YS9GZXRhbF9pbW11bmUvbXllbG9pZF9tZWZpc3RvX21vZGVsLmhkZjVcIlxubXllbG9pZF9tb2ZhX3RyYWluZWQgPC0gcnVuX21vZmEobXllbG9pZF9tb2ZhLCBvdXRmaWxlID0gb3V0ZmlsZSlcbmBgYCJ9 -->
```r
outfile <- "/nfs/team205/ed6/data/Fetal_immune/myeloid_mefisto_model.hdf5"
myeloid_mofa_trained <- run_mofa(myeloid_mofa, outfile = outfile)Load trained model
```r
myeloid_mofa_trained <- load_model(outfile, load_interpol_Z = TRUE)```
---
title: "MEFISTO on pan-fetal immune"
output: html_notebook
---
```{r}
suppressPackageStartupMessages({
  library(tidyverse)
  library(MOFA2)
  library(Matrix)
  library(SingleCellExperiment)
  library(scran)}
  )
```

## Load pseudobulked data

```{r}
indir <- "/nfs/team205/ed6/data/Fetal_immune/LMM_data/LMM_input_MYELOID_PBULK/"

matrix <- readMM(file = paste0(indir, "matrix.mtx.gz"))
coldata <- read.csv(file = paste0(indir, "metadata.csv.gz")) %>%
  column_to_rownames("X")
rowdata <- read.csv(file = paste0(indir, "gene.csv.gz")) 

## Make SingleCellExperiment obj
myeloid_sce <- SingleCellExperiment(list(logcounts = t(matrix)), colData = coldata)
rownames(myeloid_sce) <- make.unique(rowdata$GeneName) 
```


## Preprocessing

Exclude celltypes present in just one organ
```{r}
keep_ct <- data.frame(colData(myeloid_sce)) %>%
  select(organ, anno_lvl_2) %>%
  distinct() %>%
  group_by(anno_lvl_2) %>%
  summarise(n=n()) %>%
  ungroup() %>%
  filter(n > 1) %>%
  pull(anno_lvl_2)

myeloid_sce <- myeloid_sce[,myeloid_sce$anno_lvl_2 %in% keep_ct]
myeloid_sce
```

Feature selection: options here

- select HVGS with scran
- use same HVGS used for clustering
(these options don't maximize variation between organs)
- **Find HVGS within each celltype and take union**

```{r}
# all_obs <- read_csv("/nfs/team205/ed6/data/Fetal_immune/PAN.A01.v01.entire_data_normalised_log.wGut.batchCorrected_20210118.")

## Feature selection w scran WITHIN CELLTYPE
anno_groups <- split(colnames(myeloid_sce), myeloid_sce$anno_lvl_2)
all_hvgs <- c()
for (i in anno_groups){
  dec <- modelGeneVar(myeloid_sce[,i])
  hvgs <- getTopHVGs(dec, n = 1000)
  all_hvgs <- union(all_hvgs, hvgs)
  }

myeloid_sce <- myeloid_sce[all_hvgs,]
myeloid_sce <- myeloid_sce[which(rowSums(logcounts(myeloid_sce)) > 0),]
myeloid_sce
```

Scale
```{r}
assay(myeloid_sce, "scaled_logcounts") <- t(scale(t(logcounts(myeloid_sce))))
```

EDA with PCA
```{r, fig.height=15, fig.width=15}
library(scater)
myeloid_sce <- runPCA(myeloid_sce, scale=FALSE, ncomponents=30)

## Variance explained
percent.var <- attr(reducedDim(myeloid_sce), "percentVar")
plot(percent.var, log="y", xlab="PC", ylab="Variance explained (%)")

plotPCA(myeloid_sce, colour_by="organ", ncomponents=6)
plotPCA(myeloid_sce, colour_by="anno_lvl_2", ncomponents=6)
```
```{r}
plotPCA(myeloid_sce, colour_by="anno_lvl_2", text_by="anno_lvl_2")
plotPCA(myeloid_sce, colour_by="organ", text_by="anno_lvl_2")
```

Minimize obvious technical effects (3GEX/5GEX) using linear regression (following procedure from [OSCA](https://bioconductor.org/books/release/OSCA/integrating-datasets.html#linear-regression))

```{r}
library(batchelor)
myeloid_sce_3gex <- myeloid_sce[,myeloid_sce$method=="3GEX"]
myeloid_sce_5gex <- myeloid_sce[,myeloid_sce$method=="5GEX"]

set.seed(10001)
residuals <- regressBatches(myeloid_sce_3gex, myeloid_sce_5gex, d=30,
    assay.type = "logcounts",
    correct.all=TRUE,
    BSPARAM=BiocSingular::RandomParam())

assay(myeloid_sce, "scaled_logcounts") <- as.matrix(assay(residuals[,colnames(myeloid_sce)], "corrected"))

```

# Model 1 - Normal MOFA / organs as views

Make MOFA object. Use
- Organ as view
- Celltypes as groups

```{r}
## Split into one matrix x organ
org_ixs <- split(colnames(myeloid_sce), myeloid_sce$organ)
data <- lapply(org_ixs, function(i) assay(myeloid_sce[,i], "scaled_logcounts"))
data <- lapply(data, as.matrix)

## Collapse measurements from the same donor/library prep
new_sample <- paste(myeloid_sce$donor, myeloid_sce$method,myeloid_sce$anno_lvl_2, sep="-")
newsample_ixs <- split(new_sample, myeloid_sce$organ)
for (o in names(data)){
  colnames(data[[o]]) <- newsample_ixs[[o]]
}

## Fill missing values
sample_names_unique <- unique(new_sample)
for (o in names(data)){
  for (s in sample_names_unique){
    if (!s %in% colnames(data[[o]])) {
      m <- matrix(NA, nrow=nrow(data[[o]]))
      colnames(m) <- s
      data[[o]] <- cbind(data[[o]], m)
    }
  }
  data[[o]] <- data[[o]][,sample_names_unique]
}

## Vector for group assignment
groups <- sapply(strsplit(sample_names_unique, "-"), function(x) x[3])

myeloid_mofa <- create_mofa_from_matrix(data, groups = groups)
myeloid_mofa
```

## Regular MOFA
```{r}
myeloid_mofa
```

Prepare 4 training

```{r}
data_opts <- get_default_data_options(myeloid_mofa)
data_opts$center_groups <- FALSE

model_opts <- get_default_model_options(myeloid_mofa)
model_opts$num_factors <- 20

train_opts <- get_default_training_options(myeloid_mofa)
train_opts$seed <- 2020
train_opts$convergence_mode <- "fast" # use "fast" for faster training
train_opts$stochastic <- FALSE

mefisto_opts <- get_default_mefisto_options(myeloid_mofa)
mefisto_opts$warping <- FALSE
# mefisto_opts$sparseGP <- TRUE

myeloid_mofa <- prepare_mofa(
  object = myeloid_mofa,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts,
  mefisto_options = mefisto_opts
) 
```

## Train

```{r}
outfile <- "/nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model.hdf5"
myeloid_mofa_trained <- run_mofa(myeloid_mofa, outfile = outfile)
```

## Load trained model
```{r}
myeloid_mofa_trained <- load_model(outfile)
```
```{r, fig.width=15, fig.height=5}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 1:10)
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 11:20)
```


Plot weights correlation across organs (are the same genes related to a factor)

```{r}
for (f in colnames(myeloid_mofa_trained@expectations$W$BM)){
  cormat <- cor(sapply(get_weights(myeloid_mofa_trained,  factors = f, as.data.frame = FALSE), function(v) v[,1])) 
  pheatmap::pheatmap(cormat, main=f, cluster_rows = FALSE, cluster_cols = FALSE)
}
```

Add some covariates

```{r}
samples_metadata(myeloid_mofa_trained)  <- samples_metadata(myeloid_mofa_trained) %>%
  mutate(method=sapply(str_split(sample,"-"), function(x) x[2]),
         donor=sapply(str_split(sample,"-"), function(x) x[1]))

## Vector for time assignment
times <- distinct(data.frame(age=myeloid_sce$age, new_sample)) %>%
  column_to_rownames('new_sample') %>%
  .[sample_names_unique,]

samples_metadata(myeloid_mofa_trained)[["time"]] <- times
```

### Factor 1
```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 1)
```


```{r, fig.width=15, fig.height=8}
library(RColorBrewer)

get_view_genes <- function(myeloid_mofa_trained, view, factor, nfeatures=50){
  genes <- get_weights(myeloid_mofa_trained, views=view, factors = factor, as.data.frame = TRUE) %>%
  mutate(feature=str_remove(feature, "_.+")) %>%
  mutate(rank=rank(abs(value))) %>%
  top_n(nfeatures, rank) %>%
  pull(feature)
  return(genes)
}

plot_view_genes <- function(gs, filter_anno=NULL){
  if (is.null(filter_anno)) {
    pl_mat <- assay(myeloid_sce[gs,], "scaled_logcounts")
  } else {
    pl_mat <- assay(myeloid_sce[gs,myeloid_sce$anno_lvl_2 %in% filter_anno], "scaled_logcounts")
  }
  pl_anno <- data.frame(organ=myeloid_sce$organ, annotation=myeloid_sce$anno_lvl_2)
  rownames(pl_anno) <- colnames(myeloid_sce)
  
  print(pheatmap::pheatmap(pl_mat, annotation_col=pl_anno, show_colnames = FALSE, 
                     annotation_colors = list(organ=setNames(brewer.pal(9, "Spectral"), unique(pl_anno$organ)))))
}

gs <- get_view_genes(myeloid_mofa_trained, view="BM", factor=1)
plot_view_genes(gs)
```

### Factor 4

```{r, fig.width=15, fig.height=8}
gs <- get_view_genes(myeloid_mofa_trained, view="TH", factor=4)
plot_view_genes(gs)
plot_view_genes(gs, filter_anno = c("DC1", "DC_progen", "DC2"))
```

## Factor 6

```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 6)
```

```{r}
plot_factor(myeloid_mofa_trained, factors = 6, color_by = "method")
```
```{r}
plot_data_heatmap(myeloid_mofa_trained, view="BM", factor = 6)
```


```{r, fig.width=15, fig.height=8}
gs <- get_view_genes(myeloid_mofa_trained, view="BM", factor=6)
plot_view_genes(gs)
plot_view_genes(gs, filter_anno = c("DC3"))
```

## Factor 7

```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 7)
```

```{r, fig.height=10, fig.width=15}
plot_data_heatmap(myeloid_mofa_trained, view="TH", factor = 7, annotation_samples = "group")
gs <- get_view_genes(myeloid_mofa_trained, view="TH", factor=7)
plot_view_genes(gs)
```


## Factor 8

```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 8)
```

```{r, fig.height=10, fig.width=18}
plot_data_heatmap(myeloid_mofa_trained, view="BM", factor = 8, annotation_samples = "group")
plot_data_heatmap(myeloid_mofa_trained, view="LI", factor = 8, annotation_samples = "group")
plot_data_heatmap(myeloid_mofa_trained, view="SK", factor = 8, annotation_samples = "group")
gs <- get_view_genes(myeloid_mofa_trained, view="BM", factor = 8)
plot_view_genes(gs)
```
```{r}
# sapply(get_weights(myeloid_mofa_trained,  factors = 8, as.data.frame = FALSE), function(v) v[,1]) 
```

## Factor 9

```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 9)
```
```{r, fig.width=10, fig.height=3}
plot_factor(myeloid_mofa_trained, factors = 9, group_by =  "time", color_by = "group",  add_boxplot = TRUE, dodge = TRUE)
```
```{r, fig.height=10, fig.width=18}
plot_data_heatmap(myeloid_mofa_trained, view="BM", factor = 9, annotation_samples = "group")
plot_data_heatmap(myeloid_mofa_trained, view="SP", factor = 9, annotation_samples = "group")
plot_data_heatmap(myeloid_mofa_trained, view="SK", factor = 9, annotation_samples = "group")
gs <- get_view_genes(myeloid_mofa_trained, view="BM", factor = 9)
plot_view_genes(gs, filter_anno = c("promonocyte", "Promyelocyte", "DC3", "GMP"))
```

## Factor 11

```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 11)
```
```{r, fig.width=10, fig.height=3}
plot_factor(myeloid_mofa_trained, factors = 11, group_by =  "time", color_by = "group")
```
## Factor 12

```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 12)
```
```{r, fig.width=10, fig.height=3}
plot_factor(myeloid_mofa_trained, factors = 12, color_by = "group")
```
```{r, fig.height=10, fig.width=18}
plot_data_heatmap(myeloid_mofa_trained, view="SK", factor = 12, annotation_samples = "group")
gs <- get_view_genes(myeloid_mofa_trained, view="SK", factor = 12)
plot_view_genes(gs)
```
```{r}
plot_top_weights(myeloid_mofa_trained, view="SK", factor = 12)
plot_data_scatter(myeloid_mofa_trained, 
  view = "SK", 
  factor = 12, 
  features = 10,         # Number of features to show
  sign = "positive",     # select top 6 features with positive weights
  color_by = "group",  # color cells by lineage
  add_lm = T,          # add linear regression estimates
  lm_per_group = F, 
  dot_size = 2
)
```

## Factor 13

```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 13)
```

```{r, fig.width=10, fig.height=3}
plot_factor(myeloid_mofa_trained, factors = 13, color_by = "group")
```

## Factor 14

```{r}
plot_variance_explained(myeloid_mofa_trained, y = "group", factors = 14)
```

```{r, fig.width=10, fig.height=3}
plot_factor(myeloid_mofa_trained, factors = 14, color_by = "group")
```

```{r, fig.height=10, fig.width=18}
plot_data_heatmap(myeloid_mofa_trained, view="YS", factor = 14, annotation_samples = "group")
gs <- get_view_genes(myeloid_mofa_trained, view="YS", factor = 14)
plot_view_genes(gs)
```

## Make signatures from latent factors

```{r}
get_weights()
```

# Model 2 - Normal MOFA / only celltypes as groups

Make MOFA object. Use
- Celltypes as groups

```{r}
myeloid_mofa <- create_mofa_from_SingleCellExperiment(myeloid_sce, assay = "scaled_logcounts", groups = "anno_lvl_2")
myeloid_mofa
```

Prepare 4 training

```{r}
data_opts <- get_default_data_options(myeloid_mofa)
data_opts$center_groups <- FALSE

model_opts <- get_default_model_options(myeloid_mofa)
model_opts$num_factors <- 20

train_opts <- get_default_training_options(myeloid_mofa)
train_opts$seed <- 2020
train_opts$convergence_mode <- "medium" # use "fast" for faster training
train_opts$stochastic <- FALSE

mefisto_opts <- get_default_mefisto_options(myeloid_mofa)
mefisto_opts$warping <- FALSE
# mefisto_opts$sparseGP <- TRUE

myeloid_mofa <- prepare_mofa(
  object = myeloid_mofa,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts,
  mefisto_options = mefisto_opts
) 
```

## Train

```{r}
outfile <- "/nfs/team205/ed6/data/Fetal_immune/myeloid_mofa_model_oneview.hdf5"
myeloid_mofa_trained <- run_mofa(myeloid_mofa, outfile = outfile)
```

```{r}
myeloid_mofa_trained <- load_model(outfile)
```
Add some covariates

```{r}

samples_metadata(myeloid_mofa_trained)  <- samples_metadata(myeloid_mofa_trained) %>%
  mutate(time=myeloid_sce[,match(samples_metadata(myeloid_mofa_trained)$sample, colnames(myeloid_sce))]$age,
         method=myeloid_sce[,match(samples_metadata(myeloid_mofa_trained)$sample, colnames(myeloid_sce))]$method,
         donor=myeloid_sce[,match(samples_metadata(myeloid_mofa_trained)$sample, colnames(myeloid_sce))]$donor,
         organ=myeloid_sce[,match(samples_metadata(myeloid_mofa_trained)$sample, colnames(myeloid_sce))]$organ)
```

```{r}
plot_variance_explained(myeloid_mofa_trained, x='factor', y='group', split_by = 'view', plot_total = TRUE)[[1]] +
  theme(axis.text.x = element_text(angle=45, hjust=1))

get_variance_explained(myeloid_mofa_trained, as.data.frame = TRUE)[[2]] %>%
  ggplot(aes(group, value)) +
  geom_col() +
  coord_flip() +
  ylab("Var. (%)") +
  theme_classic(base_size=14)
```

Plot by celltype
```{r, fig.width=10, fig.height=10}
get_variance_explained(myeloid_mofa_trained, as.data.frame = TRUE)[[1]] %>%
  ggplot(aes(factor, value)) + geom_col() +
  coord_flip() +
  facet_wrap(group~., ncol = 6, scales = "free_x")
```
```{r}
get_variance_explained(myeloid_mofa_trained, as.data.frame = TRUE, views = "scaled_logcounts")[[1]] %>%
  pivot_wider(id_cols=c(group), names_from=factor, values_from=value) %>%
  column_to_rownames("group") %>%
  as.matrix() %>%
  t() %>%
  cor() %>%
  pheatmap::pheatmap()
```


```{r, fig.width=15, fig.height=4}
for (f in colnames(myeloid_mofa_trained@expectations$W$scaled_logcounts)){
  print(plot_factor(myeloid_mofa_trained, factors = f, group_by = "group", color_by = "organ", dot_size = 0.8, add_boxplot = TRUE, dodge = TRUE))
}
```

Insights so far:

1. Factor1:
2. Factor2:
3. Factor3: MAST/Baso/Eo vs rest
4. Factor4: DC1 signature, TH specific
5. Factor5: MAST/Baso/Eo, low in BM
6. Factor6: progenitors signal, correlated with age (progenitor maturation?)
7. Factor7: differences within ERY_MAC, more expression of hemoglobin genes in liver and bone marrow than SK
8/9. Factors 8 and 9 look technical (heatshock proteins)
10. CLP vs GMP signature (BM vs SP and LI)
14. Factor 14: GU specific DC and Mono signature


```{r}
plot_factor_ordered <- function(myeloid_mofa_trained, f){
  get_factors(myeloid_mofa_trained, factors = f, as.data.frame = TRUE) %>%
    mutate(organ = sapply(str_split(sample, "-"), function(x) x[length(x)-3])) %>%
    group_by(group) %>%
    mutate(gr_mean = median(value)) %>%
    ungroup() %>%
    arrange(gr_mean) %>%
    mutate(group=factor(group, levels=unique(group))) %>%
    ggplot(aes(group, value, color=group)) +
    geom_boxplot() +
    geom_jitter() +
    geom_hline(yintercept = 0, linetype=2) +
    coord_flip()
}

plot_factor_ordered(myeloid_mofa_trained, f=1)
plot_factor_ordered(myeloid_mofa_trained, f=2)
plot_factor_ordered(myeloid_mofa_trained, f=3)
plot_factor_ordered(myeloid_mofa_trained, f=7)
plot_factor_ordered(myeloid_mofa_trained, f=10)
plot_factor_ordered(myeloid_mofa_trained, f=14)
```
```{r, fig.width=15, fig.height=5}
plot_factor(myeloid_mofa_trained, factors = 10, color_by = "organ", group_by = "group", dodge = TRUE, dot_size = 2, add_boxplot = TRUE)
plot_weights(myeloid_mofa_trained, factors = 4, nfeatures = 50, text_size = 6)
plot_weights_scatter(myeloid_mofa_trained, factors = 9:10)
```


<!-- ```{r, fig.width=15, fig.height=5} -->
<!-- get_factors(myeloid_mofa_trained, factors = 3, as.data.frame = TRUE) %>% -->
<!--   mutate(organ = sapply(str_split(sample, "-"), function(x) x[length(x)-3])) %>% -->
<!--   group_by(group) %>% -->
<!--   mutate(gr_mean = median(value)) %>% -->
<!--   ungroup() %>% -->
<!--   arrange(gr_mean) %>% -->
<!--   mutate(group=factor(group, levels=unique(group))) %>% -->
<!--   ggplot(aes(organ, value, color=organ)) + -->
<!--   geom_boxplot() + -->
<!--   geom_jitter() + -->
<!--   # geom_hline(yintercept = 0, linetype=2) + -->
<!--   coord_flip() + -->
<!--   facet_wrap(.~group, scales = "free_x") -->
<!--             group_by = "group",  dot_size = 0.8, add_boxplot = TRUE, dodge = TRUE) + -->
<!--   coord_flip() -->
<!-- ``` -->


## Go by celltype instead of factor

### DC1
```{r}
get_variance_explained(myeloid_mofa_trained, as.data.frame = TRUE)[[1]] %>%
  filter(group=="DC1") %>%
  ggplot(aes(factor, value)) + geom_col() +
  coord_flip() +
  facet_wrap(group~., ncol = 6, scales = "free_x")
```
```{r}
plot_factors(myeloid_mofa_trained, factors = c(2,4), color_by = "organ", groups = "DC1")
```

```{r, fig.width=12, fig.height=4}
plot_factor(myeloid_mofa_trained, factors = c(4), color_by = "organ", group_by = "organ", groups = "DC1")
plot_factor(myeloid_mofa_trained, factors = 4, group_by = "group", color_by = "organ", dot_size = 0.8, add_boxplot = TRUE, dodge = TRUE)
```
```{r}
plot_weights(myeloid_mofa_trained, factors = 4, nfeatures = 30)
```
```{r}
plot_data_scatter(myeloid_mofa_trained, factor = 4, groups="DC1", color="organ", features="HLA-DRA")
```

## Explore by factor
```{r}
plot_factor(myeloid_mofa_trained, factor = 3)
plot_weights(myeloid_mofa_trained, factor = 3, nfeatures = 20)
```


## Find factors that discriminate between organs


```{r}
get_organ_AUC <- function(myeloid_mofa_trained, f, gr){
  f_df <- get_factors(myeloid_mofa_trained, factors = f, groups = gr, as.data.frame = TRUE) %>%
    # group_by(group) %>%
    # mutate(value=scale(value)) %>%
    # ungroup() %>%
    mutate(organ = sapply(str_split(sample, "-"), function(x) x[length(x)-3])) 
  organs <- unique(f_df$organ)
  suppressWarnings(suppressMessages({org_auc <- sapply(organs, function(org) roc(as.numeric(f_df$organ==org), f_df$value)$auc)}))
  all_organs <- as.character(unique(myeloid_mofa_trained@samples_metadata$organ))
  org_auc <- setNames(org_auc[all_organs], all_organs)
  return(org_auc)
}

all_organs <- as.character(unique(myeloid_mofa_trained@samples_metadata$organ))
all_groups <- as.character(unique(myeloid_mofa_trained@samples_metadata$group))

## Mask if too little samples
n_samples_mat <- samples_metadata(myeloid_mofa_trained) %>%
  group_by(organ, group) %>%
  summarise(n_samples=n()) %>%
  pivot_wider(id_cols=c(group), names_from="organ", values_from="n_samples", values_fill=0) %>%
  column_to_rownames("group") %>%
  as.matrix()

mask_pairs <- t(n_samples_mat < 3)

AUC_mat <- sapply(all_groups, function(g) get_organ_AUC(myeloid_mofa_trained, f=10, gr=g))
AUC_mat[mask_pairs[rownames(AUC_mat), colnames(AUC_mat)]] <- NA

AUC_thresh = 0.8
reshape2::melt(AUC_mat, varnames=c("organ", "group"), value.name="AUC") %>%
  ggplot(aes(organ, group)) +
  geom_point(aes(size=AUC, color=AUC)) +
  geom_point(data=. %>% filter(AUC > AUC_thresh), shape=8, size=2,color="white") +
  scale_size(limits = c(0.5,1)) +
  scale_color_gradientn(colours = RColorBrewer::brewer.pal(5, "Reds"))
```


```{r, fig.width=15, fig.height=4}
library(patchwork)
plot_factor(myeloid_mofa_trained, factors = 5, group_by = "group", color_by = "organ", dodge = TRUE, add_boxplot = TRUE) 

  plot_layout(guides="collect")

```
```{r}
plot_weights(myeloid_mofa_trained, factors = 5, nfeatures = 30)
```
```{r}
plot_data_heatmap(myeloid_mofa_trained, factor = 5, show_colnames=FALSE)
```



# Model 3 -  MEFISTO 

Add time as covariate to run MEFISTO

```{r}
## Vector for time assignment
times <- distinct(data.frame(age=myeloid_sce$age, new_sample)) %>%
  column_to_rownames('new_sample') %>%
  .[sample_names_unique,]

samples_metadata(myeloid_mofa)[["time"]] <- times

myeloid_mofa <- set_covariates(myeloid_mofa, covariates = "time")
myeloid_mofa
```
```{r, fig.height=15, fig.width=10}
gg_input <- plot_data_overview(myeloid_mofa,
                               show_covariate = TRUE,
                               show_dimensions = TRUE) 
gg_input
```

<!-- Keep groups that span multiple views -->
<!-- ```{r} -->
<!-- gr_samples <- split(samples_metadata(myeloid_mofa)$sample, samples_metadata(myeloid_mofa)$group) -->
<!-- all(is.na(data$BM[,gr_samples$Basophil])) -->
<!-- lapply(unique(samples_metadata(myeloid_mofa)[["group"]]), function(x) data$BM[]) -->


<!-- myeloid_mofa@data -->
<!-- subse(myeloid_mofa)[,samples_metadata(myeloid_mofa)[["group"]] == "Basophil"] -->
<!-- ``` -->

Prepare 4 training

```{r}
data_opts <- get_default_data_options(myeloid_mofa)

model_opts <- get_default_model_options(myeloid_mofa)
model_opts$num_factors <- 10

train_opts <- get_default_training_options(myeloid_mofa)
train_opts$seed <- 2020
train_opts$convergence_mode <- "fast" # use "fast" for faster training

mefisto_opts <- get_default_mefisto_options(myeloid_mofa)
mefisto_opts$warping <- FALSE
# mefisto_opts$sparseGP <- TRUE

myeloid_mofa <- prepare_mofa(
  object = myeloid_mofa,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts,
  mefisto_options = mefisto_opts
) 
```

## Train

```{r}
outfile <- "/nfs/team205/ed6/data/Fetal_immune/myeloid_mefisto_model.hdf5"
myeloid_mofa_trained <- run_mofa(myeloid_mofa, outfile = outfile)
```

## Load trained model
```{r}
myeloid_mofa_trained <- load_model(outfile, load_interpol_Z = TRUE)
```

